Monte Carlo Methods in Bayesian Computation:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2000
|
| Series: | Springer Series in Statistics
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications |
| Physical Description: | 1 Online-Ressource (XIII, 387 p) |
| ISBN: | 9781461212768 9781461270744 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4612-1276-8 |
Staff View
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:05Z |
| institution | BVB |
| isbn | 9781461212768 9781461270744 |
| issn | 0172-7397 |
| language | English |
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| publishDate | 2000 |
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| spelling | Chen, Ming-Hui Verfasser aut Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim New York, NY Springer New York 2000 1 Online-Ressource (XIII, 387 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 Sampling from the posterior distribution and computing posterior quanti ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistics and Computing/Statistics Programs Statistik Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf Monte-Carlo-Simulation rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 s 1\p DE-604 Monte-Carlo-Simulation s 2\p DE-604 Shao, Qi-Man Sonstige oth Ibrahim, Joseph G. Sonstige oth https://doi.org/10.1007/978-1-4612-1276-8 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Chen, Ming-Hui Monte Carlo Methods in Bayesian Computation Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistics and Computing/Statistics Programs Statistik Monte-Carlo-Simulation (DE-588)4240945-7 gnd Monte-Carlo-Simulation |
| subject_GND | (DE-588)4240945-7 |
| title | Monte Carlo Methods in Bayesian Computation |
| title_auth | Monte Carlo Methods in Bayesian Computation |
| title_exact_search | Monte Carlo Methods in Bayesian Computation |
| title_full | Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim |
| title_fullStr | Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim |
| title_full_unstemmed | Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim |
| title_short | Monte Carlo Methods in Bayesian Computation |
| title_sort | monte carlo methods in bayesian computation |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistics and Computing/Statistics Programs Statistik Monte-Carlo-Simulation (DE-588)4240945-7 gnd Monte-Carlo-Simulation |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistics and Computing/Statistics Programs Statistik Monte-Carlo-Simulation |
| url | https://doi.org/10.1007/978-1-4612-1276-8 |
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